Shifts between biotic and physical driving forces of species organization under natural disturbance regimes
Bibliographic record
Abstract
The high ecological values (i.e., the benefits that space, water, minerals, biota, and all other factors that make up natural ecosystems provide to support native life forms) and diversities found in tropical islands emphasize the importance of incorporating disturbance into ecological models. This is of major concern in appreciating how species will survive and adapt to changes and the consequences expected in terms of biodiversity. We predicted that in lotic systems, modification to natural disturbance regimes (fluvial action) would have strong consequences on community organization, with strong disturbance regimes reducing species competitive exclusion through changes in space occupation. We tested this prediction by relating microdistribution data from a crustacean species ( Atya innocous , Decapoda, Atyidae) in small and large rivers in Guadeloupe to two, independently obtained sets of explanatory variables describing the physical environment, as well as the crustacean and fish competitors. Our results show that in rivers with high-energy flow, the driving forces for species coexistence were mostly environmental, whereas in rivers with low-energy flow, biotic interactions were prevalent. These differences linked to natural disturbance regimes revealed that disturbance was a stochastic factor, overlying the classical community-structuring factors and affecting global species relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".